Short-run performance analysis using Technical Analysis on the
selected stocks
We will be fetching the data from the website (Yahoo) on selected
stock.
Selected stocks - ICICI Bank Limited and Nifty 50
List of packages
library(quantmod)
## Loading required package: xts
## Loading required package: zoo
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## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
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## as.Date, as.Date.numeric
## Loading required package: TTR
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## method from
## as.zoo.data.frame zoo
library(xts)
library(rvest)
library(tidyverse)
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## ✔ purrr 1.0.2
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(stringr)
library(forcats)
library(lubridate)
library(plotly)
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library(dplyr)
library(PerformanceAnalytics)
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## Attaching package: 'PerformanceAnalytics'
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## legend
Let us start date and end date to fetch the data from Yahoo
start <- as.Date("2022-04-01")
end <- as.Date("2023-03-31")
getSymbols(c("ICICIBANK.NS", "^NSEI"), src = "yahoo", from = start, to = end)
## [1] "ICICIBANK.NS" "NSEI"
Exploring the dataset
ICICI Bank LIMITED
head(ICICIBANK.NS)
## ICICIBANK.NS.Open ICICIBANK.NS.High ICICIBANK.NS.Low
## 2022-04-01 725.00 738.30 723.1
## 2022-04-04 742.00 749.80 725.0
## 2022-04-05 746.80 755.45 738.0
## 2022-04-06 732.30 744.90 732.1
## 2022-04-07 737.10 757.80 737.1
## 2022-04-08 749.85 756.00 743.6
## ICICIBANK.NS.Close ICICIBANK.NS.Volume ICICIBANK.NS.Adjusted
## 2022-04-01 736.25 12658848 725.8832
## 2022-04-04 746.60 17192197 736.0874
## 2022-04-05 741.80 12290391 731.3550
## 2022-04-06 741.60 13518142 731.1578
## 2022-04-07 748.55 15087175 738.0100
## 2022-04-08 754.30 15480705 743.6790
tail(ICICIBANK.NS)
## ICICIBANK.NS.Open ICICIBANK.NS.High ICICIBANK.NS.Low
## 2023-03-22 857.65 862.55 851.00
## 2023-03-23 857.00 867.45 852.80
## 2023-03-24 852.50 860.00 849.15
## 2023-03-27 854.95 856.40 844.05
## 2023-03-28 855.55 858.90 848.20
## 2023-03-29 857.05 862.10 848.00
## ICICIBANK.NS.Close ICICIBANK.NS.Volume ICICIBANK.NS.Adjusted
## 2023-03-22 860.20 17431520 853.1772
## 2023-03-23 855.45 17920621 848.4660
## 2023-03-24 852.40 17295696 845.4409
## 2023-03-27 848.70 17047675 841.7711
## 2023-03-28 854.75 24469018 847.7717
## 2023-03-29 851.00 34881515 844.0524
plot(ICICIBANK.NS[,"ICICIBANK.NS.Close"], main = "ICICIBANK PRICE MOVEMENT")
Simple Moving Average
candleChart(ICICIBANK.NS, up.col="green", dn.col="red", theme= "white")
addSMA(n=20)
addSMA(n=c(20,50,200), col = c("green", "red", "blue"))
Exploring the dataset
NIFTY 50
head(NSEI)
## NSEI.Open NSEI.High NSEI.Low NSEI.Close NSEI.Volume NSEI.Adjusted
## 2022-04-01 17436.90 17703.70 17422.70 17670.45 291800 17670.45
## 2022-04-04 17809.10 18114.65 17791.40 18053.40 345500 18053.40
## 2022-04-05 18080.60 18095.45 17921.55 17957.40 283500 17957.40
## 2022-04-06 17842.75 17901.00 17779.85 17807.65 328800 17807.65
## 2022-04-07 17723.30 17787.50 17623.70 17639.55 308800 17639.55
## 2022-04-08 17698.15 17842.75 17600.55 17784.35 274400 17784.35
tail(NSEI)
## NSEI.Open NSEI.High NSEI.Low NSEI.Close NSEI.Volume NSEI.Adjusted
## 2023-03-22 17177.45 17207.25 17107.85 17151.90 0 17151.90
## 2023-03-23 17097.40 17205.40 17045.30 17076.90 219200 17076.90
## 2023-03-24 17076.20 17109.45 16917.35 16945.05 228000 16945.05
## 2023-03-27 16984.30 17091.00 16918.55 16985.70 218400 16985.70
## 2023-03-28 17031.75 17061.75 16913.75 16951.70 238800 16951.70
## 2023-03-29 16977.30 17126.15 16940.60 17080.70 345900 17080.70
plot(NSEI[,"NSEI.Close"], main = "NIFTY 50 PRICE MOVEMENT")
candleChart(NSEI, up.col="green", dn.col="red", theme= "white")
addSMA(n=c(20,50,200), col = c("green", "red", "blue"))
Let us compare two stocks
head(list(ICICIBANK.NS,NSEI))
## [[1]]
## ICICIBANK.NS.Open ICICIBANK.NS.High ICICIBANK.NS.Low
## 2022-04-01 725.00 738.30 723.10
## 2022-04-04 742.00 749.80 725.00
## 2022-04-05 746.80 755.45 738.00
## 2022-04-06 732.30 744.90 732.10
## 2022-04-07 737.10 757.80 737.10
## 2022-04-08 749.85 756.00 743.60
## 2022-04-11 752.30 765.45 751.65
## 2022-04-12 757.00 768.90 750.25
## 2022-04-13 769.60 772.55 760.70
## 2022-04-18 751.00 762.90 749.20
## ...
## 2023-03-16 829.70 831.30 810.30
## 2023-03-17 831.80 838.40 818.65
## 2023-03-20 837.00 837.80 825.00
## 2023-03-21 843.00 854.50 839.05
## 2023-03-22 857.65 862.55 851.00
## 2023-03-23 857.00 867.45 852.80
## 2023-03-24 852.50 860.00 849.15
## 2023-03-27 854.95 856.40 844.05
## 2023-03-28 855.55 858.90 848.20
## 2023-03-29 857.05 862.10 848.00
## ICICIBANK.NS.Close ICICIBANK.NS.Volume ICICIBANK.NS.Adjusted
## 2022-04-01 736.25 12658848 725.8832
## 2022-04-04 746.60 17192197 736.0874
## 2022-04-05 741.80 12290391 731.3550
## 2022-04-06 741.60 13518142 731.1578
## 2022-04-07 748.55 15087175 738.0100
## 2022-04-08 754.30 15480705 743.6790
## 2022-04-11 759.60 13216391 748.9044
## 2022-04-12 763.85 14020000 753.0945
## 2022-04-13 762.25 16119956 751.5171
## 2022-04-18 757.80 17651530 747.1298
## ...
## 2023-03-16 823.90 14987868 817.1736
## 2023-03-17 836.25 11104490 829.4228
## 2023-03-20 836.45 14838651 829.6212
## 2023-03-21 852.40 18755854 845.4409
## 2023-03-22 860.20 17431520 853.1772
## 2023-03-23 855.45 17920621 848.4660
## 2023-03-24 852.40 17295696 845.4409
## 2023-03-27 848.70 17047675 841.7711
## 2023-03-28 854.75 24469018 847.7717
## 2023-03-29 851.00 34881515 844.0524
##
## [[2]]
## NSEI.Open NSEI.High NSEI.Low NSEI.Close NSEI.Volume NSEI.Adjusted
## 2022-04-01 17436.90 17703.70 17422.70 17670.45 291800 17670.45
## 2022-04-04 17809.10 18114.65 17791.40 18053.40 345500 18053.40
## 2022-04-05 18080.60 18095.45 17921.55 17957.40 283500 17957.40
## 2022-04-06 17842.75 17901.00 17779.85 17807.65 328800 17807.65
## 2022-04-07 17723.30 17787.50 17623.70 17639.55 308800 17639.55
## 2022-04-08 17698.15 17842.75 17600.55 17784.35 274400 17784.35
## 2022-04-11 17740.90 17779.05 17650.95 17674.95 251700 17674.95
## 2022-04-12 17584.85 17595.30 17442.35 17530.30 266000 17530.30
## 2022-04-13 17599.90 17663.65 17457.40 17475.65 245100 17475.65
## 2022-04-18 17183.45 17237.75 17067.85 17173.65 376100 17173.65
## ...
## 2023-03-16 16994.65 17062.45 16850.15 16985.60 349800 16985.60
## 2023-03-17 17111.80 17145.80 16958.15 17100.05 408100 17100.05
## 2023-03-20 17066.60 17066.60 16828.35 16988.40 241800 16988.40
## 2023-03-21 17060.40 17127.70 17016.00 17107.50 246700 17107.50
## 2023-03-22 17177.45 17207.25 17107.85 17151.90 0 17151.90
## 2023-03-23 17097.40 17205.40 17045.30 17076.90 219200 17076.90
## 2023-03-24 17076.20 17109.45 16917.35 16945.05 228000 16945.05
## 2023-03-27 16984.30 17091.00 16918.55 16985.70 218400 16985.70
## 2023-03-28 17031.75 17061.75 16913.75 16951.70 238800 16951.70
## 2023-03-29 16977.30 17126.15 16940.60 17080.70 345900 17080.70
stocks <- as.xts(data.frame(ICICIBANK.NS=ICICIBANK.NS[, "ICICIBANK.NS.Close"], NSEI=NSEI[,"NSEI.Close"]))
stocks
## ICICIBANK.NS.Close NSEI.Close
## 2022-04-01 736.25 17670.45
## 2022-04-04 746.60 18053.40
## 2022-04-05 741.80 17957.40
## 2022-04-06 741.60 17807.65
## 2022-04-07 748.55 17639.55
## 2022-04-08 754.30 17784.35
## 2022-04-11 759.60 17674.95
## 2022-04-12 763.85 17530.30
## 2022-04-13 762.25 17475.65
## 2022-04-18 757.80 17173.65
## ...
## 2023-03-16 823.90 16985.60
## 2023-03-17 836.25 17100.05
## 2023-03-20 836.45 16988.40
## 2023-03-21 852.40 17107.50
## 2023-03-22 860.20 17151.90
## 2023-03-23 855.45 17076.90
## 2023-03-24 852.40 16945.05
## 2023-03-27 848.70 16985.70
## 2023-03-28 854.75 16951.70
## 2023-03-29 851.00 17080.70
head(stocks)
## ICICIBANK.NS.Close NSEI.Close
## 2022-04-01 736.25 17670.45
## 2022-04-04 746.60 18053.40
## 2022-04-05 741.80 17957.40
## 2022-04-06 741.60 17807.65
## 2022-04-07 748.55 17639.55
## 2022-04-08 754.30 17784.35
plot(as.zoo(stocks), screens = 1, lty = 1:3, xlab = "Date", ylab = "Price")
legend("right", c("ICICIBANK.NS", "NSEI"), lty = 1:3, cex = 0.5)
stock_return <- apply(stocks, 1, function(x) {x / stocks[1,]}) %>% t %>% as.xts
stock_return
## ICICIBANK.NS.Close NSEI.Close
## 2022-04-01 1.000000 1.0000000
## 2022-04-04 1.014058 1.0216718
## 2022-04-05 1.007538 1.0162390
## 2022-04-06 1.007267 1.0077644
## 2022-04-07 1.016706 0.9982514
## 2022-04-08 1.024516 1.0064458
## 2022-04-11 1.031715 1.0002547
## 2022-04-12 1.037487 0.9920688
## 2022-04-13 1.035314 0.9889760
## 2022-04-18 1.029270 0.9718853
## ...
## 2023-03-16 1.119049 0.9612432
## 2023-03-17 1.135823 0.9677202
## 2023-03-20 1.136095 0.9614017
## 2023-03-21 1.157759 0.9681418
## 2023-03-22 1.168353 0.9706545
## 2023-03-23 1.161902 0.9664101
## 2023-03-24 1.157759 0.9589485
## 2023-03-27 1.152733 0.9612489
## 2023-03-28 1.160951 0.9593247
## 2023-03-29 1.155857 0.9666251
head(stock_return)
## ICICIBANK.NS.Close NSEI.Close
## 2022-04-01 1.000000 1.0000000
## 2022-04-04 1.014058 1.0216718
## 2022-04-05 1.007538 1.0162390
## 2022-04-06 1.007267 1.0077644
## 2022-04-07 1.016706 0.9982514
## 2022-04-08 1.024516 1.0064458
plot(as.zoo(stock_return), screens = 1, lty = 1:3, xlab = "Date", ylab = "Price")
legend("topleft", c("ICICIBANK.NS", "NSEI"), lty = 1:3, cex = 0.5)
Making our returns more accurate and insightful perspective on
investment returns
stock_change <- stocks %>% log%>% diff
head(stock_change)
## ICICIBANK.NS.Close NSEI.Close
## 2022-04-01 NA NA
## 2022-04-04 0.0139597988 0.021440346
## 2022-04-05 -0.0064498852 -0.005331746
## 2022-04-06 -0.0002696673 -0.008374146
## 2022-04-07 0.0093280043 -0.009484578
## 2022-04-08 0.0076521651 0.008175251
plot(as.zoo(stock_return), screens = 1, lty = 1:3, xlab = "Date", ylab = "Price")
legend("topleft", c("ICICIBANK.NS", "NSEI"), lty = 1:3, cex = 0.5)